In this paper, We availed the property of geometic convex function generalized a kind of important sum inequality and obtained some new conclusions.
文章利用几何凸函数的性质推广了一类重要的和式不等式，得到了一些新的结论。
The paper constructs a new optimal target function for feed forward neural networks according to convex optimization theory and constraint optimization theory.
该文利用凸优化理论和约束优化理论为前馈神经网络构造出了一个新的优化目标函数。
A branch and bound methods is proposed for minimizing concave function over a convex.
本文介绍了求解凸集上凹函数最优解的一种分支定界方法。
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